To address the low efficiency and high carbon emissions of the pump station system caused by the balanced distribution of single-pump flow in the long-distance transportation of cascade pump stations for slurry pipelines, as well as the problem that existing optimization methods tend to fall into local optima when dealing with the multi-objective optimization of cascade slurry pump stations with strong coupling and multiple constraints, a multi-objective optimal scheduling method was proposed for cascade slurry pump stations based on the improved ivy optimization algorithm (MOIVYA). First, a multi-objective optimization model for cascade pump stations was constructed, incorporating constraints on slurry rheological properties and hydraulic coupling, with the objective functions of maximizing system efficiency and minimizing carbon emissions. Second, aiming at the defects of the ivy optimization algorithm (IVYA) in solving the multi-objective optimization model of cascade slurry pump stations, selection and diversity maintenance strategies under a multi-objective environment were designed, along with multi-objective-oriented individual growth and evolution strategies to balance the convergence and distribution of the solution set. The effectiveness and feasibility of the algorithm were verified based on the ZDT series of benchmark test functions for multi-objective optimization. Finally, the proposed method was applied to a four-stage slurry pump station project case of Yunnan Dahongshan Pipeline Co., Ltd., and comparative experimental studies were conducted with the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), multi-objective particle swarm optimization (MOPSO), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). Experimental results show that the MOIVYA-based optimization method has obvious advantages over NSGA-Ⅱ, MOPSO, and MOEA/D in the engineering case. After optimization, the system efficiency is increased by 1.3%, and the daily carbon emissions are reduced by 217.5 kg. It is evident that the algorithm is effective and applicable in complex engineering scheduling, providing an effective solution for engineering practice.